BICAICcapushe {capushe} | R Documentation |
AICcapushe and BICcapushe
Description
These functions return the model selected by the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC).
Usage
AICcapushe(data, n)
BICcapushe(data, n)
Arguments
data |
|
n |
|
Details
The penalty shape value should be increasing with respect to the complexity value (column 3).
The complexity values have to be positive.
n
is necessary to compute AIC and BIC criteria. n
is the size of
sample used to compute the contrast values given in the data
matrix.
Do not confuse n
with the size of the model collection which is the number
of rows of the data
matrix.
Value
model |
The model selected by AIC or BIC. |
AIC |
The corresponding value of AIC (for AICcapushe only). |
BIC |
The corresponding value of BIC (for BICcapushe only). |
Author(s)
Vincent Brault
References
http://www.math.univ-toulouse.fr/~maugis/CAPUSHE.html
http://www.math.u-psud.fr/~brault/capushe.html
Article: Baudry, J.-P., Maugis, C. and Michel, B. (2011) Slope heuristics: overview and implementation. Statistics and Computing, to appear. doi: 10.1007/ s11222-011-9236-1
See Also
capushe
for a model selection function including AIC, BIC,
the DDSE
algorithm and the Djump
algorithm.
Examples
data(datacapushe)
AICcapushe(datacapushe,n=1000)
BICcapushe(datacapushe,n=1000)